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Control Lyapunov function design via configuration-constrained polyhedral computing
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DOI：10.1016/j.automatica.2026.112896.png)
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Abstract
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This paper proposes novel approaches for designing control Lyapunov functions (CLFs) for constrained linear systems. We leverage recent configuration-constrained polyhedral computing techniques to devise piecewise affine convex CLFs. Additionally, we generalize these methods to uncertain systems with both additive and multiplicative disturbances. The proposed design methods are capable of approximating the infinite horizon value function of both nominal and min–max optimal control problems by solving a single, one-stage, convex optimization problem. As such, these methods find practical applications in explicit controller design as well as in determining terminal regions and value functions for nominal and min–max model predictive control (MPC). Numerical examples illustrate the effectiveness of this approach.
Keywords:
Linear systems
Control Lyapunov functions
Polyhedral computing
Convex optimization
Model predictive control
Uncertain control systems
Min–max model predictive control
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